• Asian Journal of Atmospheric Environment
• /
• v.9 no.2
• /
• pp.101-113
• /
• 2015

Air quality index (AQI) or air pollution index (API) is commonly used to report the level of severity of air pollution to public. A number of methods were developed in the past by various researchers/environmental agencies for determination of AQI or API but there is no universally accepted method exists, which is appropriate for all situations. Different method uses different aggregation function in calculating AQI or API and also considers different types and numbers of pollutants. The intended uses of AQI or API are to identify the poor air quality zones and public reporting for severity of exposure of poor air quality. Most of the AQI or API indices can be broadly classify as single pollutant index or multi-pollutant index with different aggregation method. Every indexing method has its own characteristic strengths and weaknesses that affect its suitability for particular applications. This paper attempt to present a review of all the major air quality indices developed worldwide.

• International Journal of High-Rise Buildings
• /
• v.6 no.1
• /
• pp.101-111
• /
• 2017

The objective of this study is to determine the effectiveness of a modified air-side economizer in improving indoor air quality (IAQ). An air-side economizer, which uses all outdoor air for cooling, affects the building's IAQ depending on the outside air quality and can significantly affect the occupants' health, leading to respiratory and heart disease. The Air Quality Index (AQI), developed by the US Environmental Protection Agency (US EPA), measures air contaminants that adversely affect human beings: PM10, PM2.5, SO2, NO2, O3, and CO. In this study, AQI is applied as a control for the operation of an air-side economizer. The simulation is analyzed, comparing the results between the differential enthalpy economizer and AQI-modified economizer. The results confirm that an AQI-modified economizer has a positive effect on IAQ. Compared to the operating differential enthalpy economizer, energy increase in an operating AQI-modified economizer is 0.65% in Shanghai and 0.8% in Seoul.

• Journal of Korean Society for Atmospheric Environment
• /
• v.22 no.4
• /
• pp.518-528
• /
• 2006

The air quality index (AQI) is an index for reporting daily or hourly air quality to the general public. The AQI focuses on health effects that can happen within a few hours or days after breathing polluted air. Many countries have their own AQI reporting systems, and the HAEI (hourly air environment index) method is now being used in Korea. In this study, in order to compare the AQI results from different methods, we applied three methods. i.e. US AQI, Canadian AQI, and Korean HAEI, to the same air quality data-base. The data-base was constructed from 10 monitoring sites in Gyeong-buk province for the last four years since 2000. Based on the results, a critical evaluation of the Korean HAEI method was made, and a number of suggestions and recommendations were presented to improve the AQI reporting system in Korea.

• Environmental Engineering Research
• /
• v.23 no.1
• /
• pp.21-27
• /
• 2018

Air pollution index (API) is used in Mainland China and includes only $SO_2$, $NO_2$ and $PM_{10}$. In 2016, air quality index (AQI) replaced API. AQI contains three more air pollutants (CO, $O_3$ and $PM_{2.5}$). Both the indices emphasize on the effect of a single pollutant, whereas the contributions of all other pollutants are ignored. Therefore, in the present work, a novel air quality index (NAQI), which emphasizes on all air pollutants, has been introduced for the first time. The results showed that there were 19 d (5.2%) in API, 28 d (7.7%) in AQI and 183 d (50.1%) in NAQI when the indices were more than 100. In API, $PM_{10}$ and $SO_2$ were regarded as the primary pollutants, whereas all five air pollutants in AQI were regarded as primary. Furthermore, four air pollutants (other than the CO) in NAQI were regarded as primary pollutants. $PM_{10}$, as being the primary pollutant, contributed greatly in these air quality indices, and accounted for 51.2% (API), 37.0% (AQI) and 52.6% (NAQI). The results also showed that particulate matter pollution was significantly high in Luzhou, where stricter pollution control measures should be implemented.

• Advances in environmental research
• /
• v.4 no.2
• /
• pp.83-104
• /
• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.440-449
• /
• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Asian Journal of Business Environment
• /
• v.10 no.4
• /
• pp.15-19
• /
• 2020

Purpose: This research aims to explore the level of air pollution in Jakarta, the epicenter of COVID-19 Pandemic in Indonesia and its surrounding provinces during the first month of the Pandemic. Research design, data and methodology: This study uses data, which have been obtained real time from API (Application Programming Interfaces) of air quality website. The measurements of Air Quality Index (AQI), temperature, humidity, and other factors from several cities and regencies in Indonesia were obtained eight times a day. The data collected have been analyzed using descriptive statistics and mapped using QGIS. Results: The finding of this study indicates that The Greater Jakarta Area experienced a decrease in pollutant levels, especially in the Bogor area. Nevertheless, some areas, such as the north Jakarta, have exhibited slow reduction. Furthermore, the regions with high COVID-19 confirmed cases have experienced a decline in AQI. Conclusions: The study concludes that the air quality of three provinces, Jakarta, Banten, and West Java, especially in cities located in the Jakarta Metropolitan Area during COVID-19 pandemic and large-scale social restrictions, is getting better. However, in some regions, the reduction of pollutant concentrations requires a longer time, as it was very high before the pandemic.

• Atmosphere
• /
• v.30 no.4
• /
• pp.439-454
• /
• 2020

This study investigates the change in the fine particulate matter (PM2.5) concentration and World Health Organization (WHO) air quality index (AQI) in East Asia (EA) under Shared Socioeconomic Pathways (SSPs). AQI is an indicator of increasing levels about health concern, divided into six categories based on PM2.5 annual concentrations. Here, we utilized the ensemble results of UKESM1, the climate model operated in Met Office, UK, for the analysis of long-term variation during the historical (1950~2014) and future (2015~2100) period. The results show that the spatial distributions of simulated PM2.5 concentrations in present-day (1995~2014) are comparable to observations. It is found that most regions in EA exceeded the WHO air quality guideline except for Japan, Mongolia regions, and the far seas during the historical period. In future scenarios containing strong air quality (SSP1-2.6, SSP5-8.5) and medium air quality (SSP2-4.5) controls, PM2.5 concentrations are substantially reduced, resulting in significant improvement in AQI until the mid-21st century. On the other hand, the mild air pollution controls in SSP3-7.0 tend to lead poor AQI in China and Korea. This study also examines impact of increased in PM2.5 concentrations on downward shortwave energy at the surface. As a result, strong air pollution controls can improve air quality through reduced PM2.5 concentrations, but lead to an additional warming in both the near and mid-term future climate over EA.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.119-133
• /
• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of the Economic Geographical Society of Korea
• /
• v.14 no.2
• /
• pp.143-156
• /
• 2011

Recently, the environmental problems become a serious social issue, there are many efforts to manage it efficiently. As one of the ways to measure the environment in quantitative index, the environmental indicators are used in decision-making process. Air Environmental Index(AEI), which is derived from the U.S. Air Quality Index(AQI), illustrates the degree of air pollution. In study as follows: to find the charateristics of administrative dongs in Seoul, correlation analysis is conducted based on the land-use patterns and daily traffic data that represent AEI and urban spatial structure of Seoul.